



'''simple docstring'''

import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)






def 				a  (     UpperCamelCase_    :    List[Any]			)       ->	List[Any]:
		snake_case__	     =model.config

		snake_case__	     =DonutSwinConfig(
		    image_size=original_config.input_size      ,					patch_size=4      ,					depths=original_config.encoder_layer      ,					num_heads=[4, 8, 16, 32]      ,					window_size=original_config.window_size      ,					embed_dim=128      ,					)
		snake_case__	     =MBartConfig(
		    is_decoder=UpperCamelCase_      ,					is_encoder_decoder=UpperCamelCase_      ,					add_cross_attention=UpperCamelCase_      ,					decoder_layers=original_config.decoder_layer      ,					max_position_embeddings=original_config.max_position_embeddings      ,					vocab_size=len(
		        model.decoder.tokenizer			)      ,					scale_embedding=UpperCamelCase_      ,					add_final_layer_norm=UpperCamelCase_      ,					)

		return encoder_config, decoder_config






def 				a  (     UpperCamelCase_    :    List[Any]			)       ->	int:
		if "encoder.model" in name:
				snake_case__	     =name.replace('encoder.model'      ,					'encoder'			)
		if "decoder.model" in name:
				snake_case__	     =name.replace('decoder.model'      ,					'decoder'			)
		if "patch_embed.proj" in name:
				snake_case__	     =name.replace('patch_embed.proj'      ,					'embeddings.patch_embeddings.projection'			)
		if "patch_embed.norm" in name:
				snake_case__	     =name.replace('patch_embed.norm'      ,					'embeddings.norm'			)
		if name.startswith('encoder'			):
				if "layers" in name:
						snake_case__	     ='encoder.' + name
				if "attn.proj" in name:
						snake_case__	     =name.replace('attn.proj'      ,					'attention.output.dense'			)
				if "attn" in name and "mask" not in name:
						snake_case__	     =name.replace('attn'      ,					'attention.self'			)
				if "norm1" in name:
						snake_case__	     =name.replace('norm1'      ,					'layernorm_before'			)
				if "norm2" in name:
						snake_case__	     =name.replace('norm2'      ,					'layernorm_after'			)
				if "mlp.fc1" in name:
						snake_case__	     =name.replace('mlp.fc1'      ,					'intermediate.dense'			)
				if "mlp.fc2" in name:
						snake_case__	     =name.replace('mlp.fc2'      ,					'output.dense'			)

				if name == "encoder.norm.weight":
						snake_case__	     ='encoder.layernorm.weight'
				if name == "encoder.norm.bias":
						snake_case__	     ='encoder.layernorm.bias'

		return name






def 				a  (     UpperCamelCase_    :    int      ,					UpperCamelCase_    :    Tuple			)       ->	Optional[int]:
		for key in orig_state_dict.copy().keys():
				snake_case__	     =orig_state_dict.pop(UpperCamelCase_			)

				if "qkv" in key:
						snake_case__	     =key.split('.'			)
						snake_case__	     =int(key_split[3]			)
						snake_case__	     =int(key_split[5]			)
						snake_case__	     =model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

						if "weight" in key:
								snake_case__	     =val[:dim, :]
								snake_case__	     =val[dim : dim * 2, :]
								snake_case__	     =val[-dim:, :]
						else:
								snake_case__	     =val[:dim]
								snake_case__	     =val[dim : dim * 2]
								snake_case__	     =val[-dim:]
				elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
						# HuggingFace implementation doesn't use attn_mask buffer
						# and model doesn't use final LayerNorms for the encoder
						pass
				else:
						snake_case__	     =val

		return orig_state_dict






def 				a  (     UpperCamelCase_    :    str      ,					UpperCamelCase_    :    Any=None      ,					UpperCamelCase_    :    int=False			)       ->	Dict:
		# load original model
		snake_case__	     =DonutModel.from_pretrained(UpperCamelCase_			).eval()

		# load HuggingFace model
		snake_case__     ,	snake_case__	     =get_configs(UpperCamelCase_			)
		snake_case__	     =DonutSwinModel(UpperCamelCase_			)
		snake_case__	     =MBartForCausalLM(UpperCamelCase_			)
		snake_case__	     =VisionEncoderDecoderModel(encoder=UpperCamelCase_      ,					decoder=UpperCamelCase_			)
		model.eval()

		snake_case__	     =original_model.state_dict()
		snake_case__	     =convert_state_dict(UpperCamelCase_      ,					UpperCamelCase_			)
		model.load_state_dict(UpperCamelCase_			)

		# verify results on scanned document
		snake_case__	     =load_dataset('hf-internal-testing/example-documents'			)
		snake_case__	     =dataset['test'][0]['image'].convert('RGB'			)

		snake_case__	     =XLMRobertaTokenizerFast.from_pretrained(UpperCamelCase_      ,					from_slow=UpperCamelCase_			)
		snake_case__	     =DonutImageProcessor(
		    do_align_long_axis=original_model.config.align_long_axis      ,					size=original_model.config.input_size[::-1]			)
		snake_case__	     =DonutProcessor(UpperCamelCase_      ,					UpperCamelCase_			)
		snake_case__	     =processor(UpperCamelCase_      ,					return_tensors='pt'			).pixel_values

		if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
				snake_case__	     ='<s_docvqa><s_question>{user_input}</s_question><s_answer>'
				snake_case__	     ='When is the coffee break?'
				snake_case__	     =task_prompt.replace('{user_input}'      ,					UpperCamelCase_			)
		elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
				snake_case__	     ='<s_rvlcdip>'
		elif model_name in [
		    "naver-clova-ix/donut-base-finetuned-cord-v1",
		    "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
		]:
				snake_case__	     ='<s_cord>'
		elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
				snake_case__	     ='s_cord-v2>'
		elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
				snake_case__	     ='<s_zhtrainticket>'
		elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
				# use a random prompt
				snake_case__	     ='hello world'
		else:
				raise ValueError('Model name not supported'			)
		snake_case__	     =original_model.decoder.tokenizer(UpperCamelCase_      ,					add_special_tokens=UpperCamelCase_      ,					return_tensors='pt'			)[
		    'input_ids'
		]

		snake_case__	     =original_model.encoder.model.patch_embed(UpperCamelCase_			)
		snake_case__     ,	snake_case__	     =model.encoder.embeddings(UpperCamelCase_			)
		assert torch.allclose(UpperCamelCase_      ,					UpperCamelCase_      ,					atol=1e-3			)

		# verify encoder hidden states
		snake_case__	     =original_model.encoder(UpperCamelCase_			)
		snake_case__	     =model.encoder(UpperCamelCase_			).last_hidden_state
		assert torch.allclose(UpperCamelCase_      ,					UpperCamelCase_      ,					atol=1e-2			)

		# verify decoder hidden states
		snake_case__	     =original_model(UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_			).logits
		snake_case__	     =model(UpperCamelCase_      ,					decoder_input_ids=UpperCamelCase_			).logits
		assert torch.allclose(UpperCamelCase_      ,					UpperCamelCase_      ,					atol=1e-3			)
		print('Looks ok!'			)

		if pytorch_dump_folder_path is not None:
				print(f"""Saving model and processor to {pytorch_dump_folder_path}"""			)
				model.save_pretrained(UpperCamelCase_			)
				processor.save_pretrained(UpperCamelCase_			)

		if push_to_hub:
				model.push_to_hub('nielsr/' + model_name.split('/'			)[-1]      ,					commit_message='Update model'			)
				processor.push_to_hub('nielsr/' + model_name.split('/'			)[-1]      ,					commit_message='Update model'			)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default='''naver-clova-ix/donut-base-finetuned-docvqa''',
			    required=False,
			    type=str,
			    help='''Name of the original model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default=None,
			    required=False,
			    type=str,
			    help='''Path to the output PyTorch model directory.''',
			)
			parser.add_argument(
			    '''--push_to_hub''',
			    action='''store_true''',
			    help='''Whether or not to push the converted model and processor to the 🤗 hub.''',
			)

			SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  parser.parse_args()
			convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''

# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class 							a__:
			a_	: CommonSchedulerState

			# setable values
			a_	: jnp.ndarray
			a_	: jnp.ndarray
			a_	: Optional[int]									=      None






			@classmethod
			def 						_lowercase  (     cls					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Dict:
					return cls(common=_UpperCAmelCase					,   init_noise_sigma=_UpperCAmelCase					,   timesteps=_UpperCAmelCase						)





@dataclass
class 							a__( snake_case__     ):
			a_	: DDPMSchedulerState





class 							a__( snake_case__   ,      snake_case__     ):
			a_	: Union[str, Any]									=      [e.name for e in FlaxKarrasDiffusionSchedulers]

			a_	: jnp.dtype






			@property
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					return True






			@register_to_config
			def __init__(     self					,   _UpperCAmelCase = 1000					,   _UpperCAmelCase = 0.0_001					,   _UpperCAmelCase = 0.02					,   _UpperCAmelCase = "linear"					,   _UpperCAmelCase = None					,   _UpperCAmelCase = "fixed_small"					,   _UpperCAmelCase = True					,   _UpperCAmelCase = "epsilon"					,   _UpperCAmelCase = jnp.floataa					,   )							->							Union[str, Any]:
					snake_case__	     =dtype






			def 						_lowercase  (     self					,   _UpperCAmelCase = None						)							->							DDPMSchedulerState:
					if common is None:
							snake_case__	     =CommonSchedulerState.create(self						)

					# standard deviation of the initial noise distribution
					snake_case__	     =jnp.array(1.0					,   dtype=self.dtype						)

					snake_case__	     =jnp.arange(0					,   self.config.num_train_timesteps						).round()[::-1]

					return DDPMSchedulerState.create(
					    common=_UpperCAmelCase					,   init_noise_sigma=_UpperCAmelCase					,   timesteps=_UpperCAmelCase					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							jnp.ndarray:
					return sample






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = ()						)							->							DDPMSchedulerState:
					snake_case__	     =self.config.num_train_timesteps // num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# rounding to avoid issues when num_inference_step is power of 3
					snake_case__	     =(jnp.arange(0					,   _UpperCAmelCase						) * step_ratio).round()[::-1]

					return state.replace(
					    num_inference_steps=_UpperCAmelCase					,   timesteps=_UpperCAmelCase					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None						)							->							Optional[Any]:
					snake_case__	     =state.common.alphas_cumprod[t]
					snake_case__	     =jnp.where(t > 0					,   state.common.alphas_cumprod[t - 1]					,   jnp.array(1.0					,   dtype=self.dtype						)						)

					# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
					# and sample from it to get previous sample
					# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
					snake_case__	     =(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

					if variance_type is None:
							snake_case__	     =self.config.variance_type

					# hacks - were probably added for training stability
					if variance_type == "fixed_small":
							snake_case__	     =jnp.clip(_UpperCAmelCase					,   a_min=1E-20						)
					# for rl-diffuser https://arxiv.org/abs/2205.09991
					elif variance_type == "fixed_small_log":
							snake_case__	     =jnp.log(jnp.clip(_UpperCAmelCase					,   a_min=1E-20						)						)
					elif variance_type == "fixed_large":
							snake_case__	     =state.common.betas[t]
					elif variance_type == "fixed_large_log":
							# Glide max_log
							snake_case__	     =jnp.log(state.common.betas[t]						)
					elif variance_type == "learned":
							return predicted_variance
					elif variance_type == "learned_range":
							snake_case__	     =variance
							snake_case__	     =state.common.betas[t]
							snake_case__	     =(predicted_variance + 1) / 2
							snake_case__	     =frac * max_log + (1 - frac) * min_log

					return variance






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   _UpperCAmelCase = True					,   )							->							Union[FlaxDDPMSchedulerOutput, Tuple]:
					snake_case__	     =timestep

					if key is None:
							snake_case__	     =jax.random.PRNGKey(0						)

					if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
							snake_case__     ,	snake_case__	     =jnp.split(_UpperCAmelCase					,   sample.shape[1]					,   axis=1						)
					else:
							snake_case__	     =None

					# 1. compute alphas, betas
					snake_case__	     =state.common.alphas_cumprod[t]
					snake_case__	     =jnp.where(t > 0					,   state.common.alphas_cumprod[t - 1]					,   jnp.array(1.0					,   dtype=self.dtype						)						)
					snake_case__	     =1 - alpha_prod_t
					snake_case__	     =1 - alpha_prod_t_prev

					# 2. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
					if self.config.prediction_type == "epsilon":
							snake_case__	     =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
					elif self.config.prediction_type == "sample":
							snake_case__	     =model_output
					elif self.config.prediction_type == "v_prediction":
							snake_case__	     =(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
					else:
							raise ValueError(
							    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
							    ' for the FlaxDDPMScheduler.'						)

					# 3. Clip "predicted x_0"
					if self.config.clip_sample:
							snake_case__	     =jnp.clip(_UpperCAmelCase					,   -1					,   1						)

					# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					snake_case__	     =(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
					snake_case__	     =state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

					# 5. Compute predicted previous sample µ_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					snake_case__	     =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

					# 6. Add noise
					def random_variance():
							snake_case__	     =jax.random.split(_UpperCAmelCase					,   num=1						)
							snake_case__	     =jax.random.normal(_UpperCAmelCase					,   shape=model_output.shape					,   dtype=self.dtype						)
							return (self._get_variance(_UpperCAmelCase					,   _UpperCAmelCase					,   predicted_variance=_UpperCAmelCase						) ** 0.5) * noise

					snake_case__	     =jnp.where(t > 0					,   random_variance()					,   jnp.zeros(model_output.shape					,   dtype=self.dtype						)						)

					snake_case__	     =pred_prev_sample + variance

					if not return_dict:
							return (pred_prev_sample, state)

					return FlaxDDPMSchedulerOutput(prev_sample=_UpperCAmelCase					,   state=_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							jnp.ndarray:
					return add_noise_common(state.common					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							jnp.ndarray:
					return get_velocity_common(state.common					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def __len__(     self						)							->							Optional[int]:
					return self.config.num_train_timesteps




